Many types of agricultural and forage products such as hay, grain, or alfalfa must be partially dried after harvest in order to enable baling for efficient transport and consumption. This drying is achieved by cutting and crimping the crop into rows called windrows, which are then allowed to dry in the sun until the desired moisture content is reached.
In commercial hay harvesting applications in areas such as the Southwestern United States, hay collection must be critically timed to ensure the windrowed hay is dried down enough to prevent baled hay from molding, but not so far that the leaf stems of the harvested hay become brittle and break during the baling process. Due to this limitation, in these dryer areas, baling can only be done in a very narrow window of time during the day, thus limiting overall productivity. In other areas where drydown is much more variable due to unpredictable weather, the quickest possible drydown is usually chosen to minimize the risk of overdrying the crop. However, this means that a harvester can only cut as much crop as he is sure can be collected before the drying process has gone too far, thus impeding overall productivity.
Certain algorithms are available that can estimate collection times for windrowed crops based on estimated inputs such as the weather forecast and known crop parameters such as the maturity of the crop at harvest. However, the results of these algorithms are general in nature and cannot account for the wide variety of microclimates that may be present in any given field of windrowed crop to be collected. Further, these algorithms cannot account for sudden changes to conditions that may impact a crop's optimal drydown time. Accordingly, there is a need for a system to optimize the collection of a windrowed crop based on all parts of a crop's field and actual conditions in that field.